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Independent Project of analysis workflow performed to study the spatial transcriptomics of a human breast cancer dataset generated using the Xenium platform. The analysis follows standard steps like:
The analysis found distinct cell populations, with invasive carcinoma cells concentrated in specific areas, potentially indicating tumor boundaries. FAS expression was notably high in these invasive regions, surprising given its role in tumor metabolism, while CEACAM6 marked ductal carcinoma areas. Immune cells were scattered, suggesting infiltration into the tumor.
The Xenium platform is a spatial transcriptomics technology that allows for the simultaneous measurement of gene expression and spatial location of cells in a tissue section. The dataset contains gene expression data from thousands of cells, as well as spatial information about the location of each cell in the tissue section. In this analysis, we will conduct a comprehensive analysis of the Dataset to identify cell types, spatial patterns, and marker genes associated with breast cancer.
Data Source:: https://www.10xgenomics.com/products/xenium-in-situ/preview-dataset-human-breast
Initial preprocessing removes low-quality cells and visualizes key metrics:
nFeature_Xenium) and transcript counts per cell
(nCount_Xenium).To correct for technical variations (e.g., sequencing depth), we
apply SCTransform, a variance-stabilizing normalization
method that accounts for gene expression dependencies on sequencing
depth. note: The method also scales the data to account for differences
in gene expression magnitude.
Dimensionality reduction simplifies the dataset while preserving biologically relevant variation:
groupeing cells based on shared expression profiles:
## Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
##
## Number of nodes: 163779
## Number of edges: 5297889
##
## Running Louvain algorithm...
## Maximum modularity in 10 random starts: 0.9488
## Number of communities: 11
## Elapsed time: 93 seconds
The above Clusters are annotated using differential expression analysis and known marker genes:
Marker Genes Used: - B cells: MS4A1, CD79A - Macrophages: ITGAX - T cells: CD3E, CD4, CD8A - NK cells: NKG7 - Mast cells: KIT - Endothelial cells: PECAM1 - Myoepithelial cells: KRT15 - Fibroblasts: LUM - Proliferating cells: MKI67 - Ductal carcinoma: CEACAM6 - Invasive tumor: FASN
Spatial plots integrate gene expression with tissue coordinates:
The analysis of the Xenium Human Breast Cancer Dataset provided key insights into cellular composition and spatial organization:
| Cell Type | Marker Genes | Reference |
|---|---|---|
| Invasive Tumor | FASN | Swinnen et al., 2006 [1] |
| Ductal Carcinoma | CEACAM6 | Blumenthal et al., 2007 [2] |
| B Cells | MS4A1, CD79A | Standard markers |
| Macrophages | ITGAX | Standard markers |
| T/NK Cells | CD3E, CD4, CD8A, NKG7 | Standard markers |
| Endothelial | PECAM1 | Standard markers |
| Myoepithelial | KRT15 | Standard markers |
| Fibroblasts | LUM | Standard markers |
| Mast Cells | KIT | Standard markers |
Spatial_TumorMarkers.png) revealed
invasive carcinoma cells concentrated in peripheral regions, potentially
marking tumor boundaries, while ductal carcinoma cells aligned with
central ductal structures (see Figure 1).Spatial_FASN.png), aligning with its role in lipid
metabolism supporting tumor growth [1].Spatial_CEACAM6.png), consistent with its association with
epithelial-derived cancers [2].These findings underscore the cellular diversity and spatial architecture of the breast cancer microenvironment, with implications for tumor progression and immune interactions.
This spatial transcriptomics analysis demonstrates the utility of the Xenium platform in dissecting the breast cancer tumor microenvironment. By combining gene expression with spatial data, I identified key cell types and their distributions, offering insights into tumor-immune interactions and potential therapeutic targets.The elevated FAS expression in invasive regions underscores its metabolic role, suggesting avenues for targeting lipid metabolism in cancer therapy. Future work could integrate additional datasets or functional assays to validate these findings and explore clinical implications.